no code implementations • 10 Feb 2023 • Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner
Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption.
1 code implementation • 10 Nov 2022 • Liansheng Wang, Jiacheng Wang, Lei Zhu, Huazhu Fu, Ping Li, Gary Cheng, Zhipeng Feng, Shuo Li, Pheng-Ann Heng
Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19.
no code implementations • 31 Oct 2022 • Hilal Asi, Karan Chadha, Gary Cheng, John Duchi
In non-private stochastic convex optimization, stochastic gradient methods converge much faster on interpolation problems -- problems where there exists a solution that simultaneously minimizes all of the sample losses -- than on non-interpolating ones; we show that generally similar improvements are impossible in the private setting.
no code implementations • 29 Aug 2022 • Peng Wu, Lipeng Gu, Xuefeng Yan, Haoran Xie, Fu Lee Wang, Gary Cheng, Mingqiang Wei
Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas.
1 code implementation • 28 Apr 2022 • Yiyang Shen, Yongzhen Wang, Mingqiang Wei, Honghua Chen, Haoran Xie, Gary Cheng, Fu Lee Wang
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions.
no code implementations • 18 Mar 2022 • Tavor Z. Baharav, Gary Cheng, Mert Pilanci, David Tse
We design an instance-adaptive algorithm that learns to sample according to the importance of each coordinate, and with probability at least $1-\delta$ returns an $\epsilon$ accurate estimate of $f(\boldsymbol{\mu})$.
1 code implementation • 1 Mar 2022 • Fu Lee Wang, Yidan Feng, Haoran Xie, Gary Cheng, Mingqiang Wei
Image filters are fast, lightweight and effective, which make these conventional wisdoms preferable as basic tools in vision tasks.
no code implementations • 16 Aug 2021 • Gary Cheng, Karan Chadha, John Duchi
We propose an asymptotic framework to analyze the performance of (personalized) federated learning algorithms.
no code implementations • 7 Jan 2021 • Karan Chadha, Gary Cheng, John C. Duchi
We extend the Approximate-Proximal Point (aProx) family of model-based methods for solving stochastic convex optimization problems, including stochastic subgradient, proximal point, and bundle methods, to the minibatch and accelerated setting.
no code implementations • NeurIPS 2020 • Hilal Asi, Karan Chadha, Gary Cheng, John C. Duchi
In contrast to standard stochastic gradient methods, these methods may have linear speedup in the minibatch setting even for non-smooth functions.
no code implementations • 10 Jul 2020 • Leonard K. M. Poon, Nevin L. Zhang, Haoran Xie, Gary Cheng
Topic modeling has been one of the most active research areas in machine learning in recent years.
2 code implementations • 6 Nov 2018 • Gary Cheng, Armin Askari, Kannan Ramchandran, Laurent El Ghaoui
In this paper, we consider the problem of selecting representatives from a data set for arbitrary supervised/unsupervised learning tasks.